3,394 research outputs found
Location based services in wireless ad hoc networks
In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii
Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring
system whose goal is to measure, detect, characterize, and track threats such
as distribute denial of service(DDoS) attacks and worms. To block the
monitoring system in the internet the attackers are targeted the ITM system. In
this paper we address flooding attack against ITM system in which the attacker
attempt to exhaust the network and ITM's resources, such as network bandwidth,
computing power, or operating system data structures by sending the malicious
traffic. We propose an information-theoretic frame work that models the
flooding attacks using Botnet on ITM. Based on this model we generalize the
flooding attacks and propose an effective attack detection using Honeypots
Consistent SDNs through Network State Fuzzing
The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Nevertheless, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to continuously test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly
Consistent SDNs through Network State Fuzzing
The conventional wisdom is that a software-defined network (SDN) operates
under the premise that the logically centralized control plane has an accurate
representation of the actual data plane state. Unfortunately, bugs,
misconfigurations, faults or attacks can introduce inconsistencies that
undermine correct operation. Previous work in this area, however, lacks a
holistic methodology to tackle this problem and thus, addresses only certain
parts of the problem. Yet, the consistency of the overall system is only as
good as its least consistent part. Motivated by an analogy of network
consistency checking with program testing, we propose to add active probe-based
network state fuzzing to our consistency check repertoire. Hereby, our system,
PAZZ, combines production traffic with active probes to periodically test if
the actual forwarding path and decision elements (on the data plane) correspond
to the expected ones (on the control plane). Our insight is that active traffic
covers the inconsistency cases beyond the ones identified by passive traffic.
PAZZ prototype was built and evaluated on topologies of varying scale and
complexity. Our results show that PAZZ requires minimal network resources to
detect persistent data plane faults through fuzzing and localize them quickly
while outperforming baseline approaches.Comment: Added three extra relevant references, the arXiv later was accepted
in IEEE Transactions of Network and Service Management (TNSM), 2019 with the
title "Towards Consistent SDNs: A Case for Network State Fuzzing
Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning
Accurately predicting individual-level infection state is of great value
since its essential role in reducing the damage of the epidemic. However, there
exists an inescapable risk of privacy leakage in the fine-grained user mobility
trajectories required by individual-level infection prediction. In this paper,
we focus on developing a framework of privacy-preserving individual-level
infection prediction based on federated learning (FL) and graph neural networks
(GNN). We propose Falcon, a Federated grAph Learning method for
privacy-preserving individual-level infeCtion predictiON. It utilizes a novel
hypergraph structure with spatio-temporal hyperedges to describe the complex
interactions between individuals and locations in the contagion process. By
organically combining the FL framework with hypergraph neural networks, the
information propagation process of the graph machine learning is able to be
divided into two stages distributed on the server and the clients,
respectively, so as to effectively protect user privacy while transmitting
high-level information. Furthermore, it elaborately designs a differential
privacy perturbation mechanism as well as a plausible pseudo location
generation approach to preserve user privacy in the graph structure. Besides,
it introduces a cooperative coupling mechanism between the individual-level
prediction model and an additional region-level model to mitigate the
detrimental impacts caused by the injected obfuscation mechanisms. Extensive
experimental results show that our methodology outperforms state-of-the-art
algorithms and is able to protect user privacy against actual privacy attacks.
Our code and datasets are available at the link:
https://github.com/wjfu99/FL-epidemic.Comment: accepted by TOI
Towards Modeling Privacy in WiFi Fingerprinting Indoor Localization and its Application
In this paper, we study privacy models for privacy-preserving WiFi fingerprint based indoor local- ization (PPIL) schemes. We show that many existing models are insufficient and make unrealistic assumptions regarding adversaries’ power. To cover the state-of-the-art practical attacks, we propose the first formal security model which formulates the security goals of both client-side and server-side privacy beyond the curious-but-honest setting. In particular, our model considers various malicious behaviors such as exposing secrets of principles, choosing malicious WiFi fingerprints in location queries, and specifying the location area of a target client. Furthermore, we formulate the client-side privacy in an indistinguishability manner where an adversary is required to distinguish a client’s real location from a random one. The server-side privacy requires that adversaries cannot generate a fab- ricate database which provides a similar function to the real database of the server. In particular, we formally define the similarity between databases with a ball approach that has not been formalized before. We show the validity and applicability of our model by applying it to analyze the security of an existing PPIL protocol. We also design experiments to test the server-privacy in the presence of database leakage, based on a candidate server-privacy attack.Peer reviewe
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